Artificial Intelligence Awareness

Free Three-day Summer Workshop (August 10 - 12) for Community College Students

Students from underrepresented groups are highly encouraged to apply for this workshop

ELIGIBILITY
Any community college student can apply for this opportunity. We encourage women students to apply for this program. We will accept applicants based on a first-come-first-served to select 20 students. No background knowledge in AI is required to apply for this workshop. So please apply for this program before all available slots are quickly filled.

ABOUT

This summer workshop is to motivate community college students (especially, students in the underrepresented groups) and make them to the nation’s future STEM workforce demands.

This NSF-Funded Summer Workshop is organized by the PIs (Dr. Chen, Dr. Cho, and Dr. Jeong) of NSF grant (III: Medium: Collaborative Research: MUDL: Multidimensional Uncertainty-Aware Deep Learning Framework).

- Dr. Feng Chen, University of Texas at Dallas
- Dr. Jin-Hee Cho, Virginia Polytechnic Institute and State University (Virginia Tech.)
- Dr. Dong Hyun Jeong, University of the District of Columbia

This is a three-day workshop with a three-hour session per day (1:00 PM - 4:00 PM, Aug. 10-12, 2022) on the main campus of the University of the District Columbia.

The application due is June 30.

Three sessions are:
  • Day 1) Computer science
  • Day 2) Artificial intelligence (deep learning)
  • Day 3) Computational, multidisciplinary research
Apply (CLOSED)

Programs in Detail

Each session starts at 1:00 PM and lasts about 3 hours (1:00 PM - 4:00 PM).

Day 1

Computer science

  • 1:00-1:20: Introduction and Perception Survey (Jin-Hee Cho) LECTURE
  • 1:20-1:45: AI-based Cybersecurity and Decision Making: Applications for Trustworthy Cyberspace (Jin-Hee Cho)
  • 1:45-2:00: Education and Career Paths in Computing (Jin-Hee Cho) LECTURE
  • 2:00-2:10: Break
  • 2:10-3:00: Experiential Learning: Basic understanding of Computer Science & AI
    • Travelling Salesman Problem (Qi Zhang) LECTURE
    • Rubik's Cube Using Deep Learning (Qisheng Zhang) LECTURE
    • Natural Language Processing (Dian Chen/Zhen Guo) LECTURE
  • 3:00-3:10: Break
  • 3:10-3:45: Invited speaker (Dr. Nandie Leslie, Raytheon) BIO
  • 3:45-4:00: Evaluation Survey and Wrap-up

Day 2

Artificial intelligence (deep learning)

  • 1:00-1:30: Deep learning and autonomous driving (Tianhao Wang)
  • 1:30-2:15: Introduction to convolutional neural networks (CNN) (Linlin Yu)
  • 2:15-2:25: Break
  • 2:25-3:15: Experiential learning: Basic understanding of deep learning models
    • Design of CNN for car detection in camera images
    • Design of artificial neural networks (ANN) for classification in toy datasets (optional)
  • 3:15-3:45: Invited speaker (Dr. Fang Jin)
  • 3:45-4:00: Evaluation Survey and Wrap-up

Day 3

Computational, multidisciplinary research

  • 1:00 - 1:20 Introduction - Applied Research LECTURE
  • 1:20 - 1:50 Invited Talk, Dr. Briana Wellman, UDC
  • 2:00 - 2:50 Digit Recognition with Neural Network LECTURE
    • Group Activity - creating digit samples
  • 3:00 - 3:40 Importance of visualization and visual analytics LECTURE
  • 3:40 - 4:00 Evaluation Survey and Wrap-up

Research Activities

Online Social Deception Research
Research Activity
Online Social Deception Research
Z. Guo and J.H. Cho
Uncertainty-Aware Deep Reinforcement Learning-based Competitive Influence Maximization Combating False Information
Research Activity
Uncertainty-Aware Deep Reinforcement Learning-based Competitive Influence Maximization Combating False Information
Q. Zhang and J.H. Cho
Computational Analysis + Visual Analysis for Network Intrusions
Research Activity
Computational Analysis + Visual Analysis for Network Intrusions
D. H. Jeong et al.
NSF IIS-1815696: A novel paradigm for detecting complex anomalous patterns in multi-modal, heterogeneous, and high-dimensional multi-source data sets
NSF IIS-1815696: A novel paradigm for detecting complex anomalous patterns in multi-modal, heterogeneous, and high-dimensional multi-source data sets
C. Feng et al.

Hosted at the University of the District of Columbia
Location: Building 42 / Room 108 & 113B
4200 Connecticut Avenue NW
Washington, DC 20008

Apply Now!